In the event of a collision, it would be desirable to achieve an optimum, stable triggering response of the installed restraint systems (air bags, seat belts, etc.) for each type of motor vehicle. In other words, the restraint systems should be triggered only when the occupants of the vehicle are actually endangered in a collision, and then the restraints should also be triggered with a very high degree of reliability. Faulty triggering of restraint systems is undesirable because it is very expensive to service the restraint systems; insurance companies in particular have a great interest in preventing faulty triggering of restraint systems as much as possible. Therefore, the algorithm controlling the triggering of restraint systems must be adapted individually to each type of vehicle. Calibration of the triggering algorithm is performed using collision data that reflect the behavior of the vehicle body in a wide variety of collision situations.
There are essentially three categories of collisions —front, side and repair collisions. Repair collisions should not trigger a restraint system, e.g., bumping of vehicles in parking. Repair collisions occur at low vehicle speeds (≦15 km/h). The number of actual collisions of a vehicle should be greatly limited for cost reasons. However, to make the triggering algorithm as reliable and stable as possible, it would be desirable to have access to a large number of collision signals that describe a wide variety of different collision situations.
It is known from the conference paper of ASL at SAE, no. 920480 that measured collision data can be used to generate new synthetic collision data by mathematical methods to describe other collision situations. However, the mathematical method used there to derive new collision signals is very complicated and requires a long computation time. Furthermore, the collisions synthesized by the known method have a poor correlation with collisions that actually occur.